coat/configs/mnasnet

• MnasNet
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment (optional)

MnasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Introduction

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Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network.

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
MnasNet-B1-0_75	D910x8-G	71.81	90.53	3.20	96s/epoch		model	cfg	log
MnasNet-B1-1_0	D910x8-G	74.28	91.70	4.42	96s/epoch		model	cfg	log
MnasNet-B1-1_4	D910x8-G	76.01	92.83	7.16	121s/epoch		model	cfg	log

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• Context: GPU_TYPE x pieces - G/F, G - graph mode, F - pynative mode with ms function.

Quick Start

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Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

Please download the ImageNet-1K dataset for model training and validation.

Training

• Hyper-parameters. The hyper-parameter configurations for producing the reported results are stored in the yaml files in mindcv/configs/mnasnet folder. For example, to train with one of these configurations, you can run:

  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  mpirun -n 8 python train.py -c configs/mnasnet/mnasnet0.75_gpu.yaml --data_dir /path/to/imagenet
  

  Note that the number of GPUs/Ascends and batch size will influence the training results. To reproduce the training result at most, it is recommended to use the same number of GPUs/Ascends with the same batch size.

Detailed adjustable parameters and their default value can be seen in config.py.

Validation

• To validate the trained model, you can use validate.py. Here is an example for mnasnet0_75 to verify the accuracy of pretrained weights.

  python validate.py 
  -c configs/mnasnet/mnasnet0.75_ascend.yaml 
  --data_dir=/path/to/imagenet 
  --ckpt_path=/path/to/ckpt
  

• To validate the model, you can use validate.py. Here is an example for mnasnet0_75 to verify the accuracy of your training.

  python validate.py 
  -c configs/mnasnet/mnasnet0.75_ascend.yaml 
  --data_dir=/path/to/imagenet 
  --ckpt_path=/path/to/ckpt
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.